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How the performance of hydrological models relates to credibility of projections under climate change

Urheber*innen
/persons/resource/Valentina.Krysanova

Krysanova,  Valentina
Potsdam Institute for Climate Impact Research;

Donelly,  C.
External Organizations;

Gelfan,  A.
External Organizations;

/persons/resource/Dieter.Gerten

Gerten,  Dieter
Potsdam Institute for Climate Impact Research;

Arheimer,  B.
External Organizations;

/persons/resource/Fred.Hattermann

Hattermann,  Fred Fokko
Potsdam Institute for Climate Impact Research;

/persons/resource/zbyszek

Kundzewicz,  Zbigniew W.
Potsdam Institute for Climate Impact Research;

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8039oa.pdf
(Verlagsversion), 3MB

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Zitation

Krysanova, V., Donelly, C., Gelfan, A., Gerten, D., Arheimer, B., Hattermann, F. F., Kundzewicz, Z. W. (2018): How the performance of hydrological models relates to credibility of projections under climate change. - Hydrological Sciences Journal, 63, 5, 696-720.
https://doi.org/10.1080/02626667.2018.1446214


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_22358
Zusammenfassung
Two approaches can be distinguished in studies of climate change impacts on water resources when accounting for issues related to impact model performance: (1) using a multi-model ensemble disregarding model performance, and (2) using models after their evaluation and considering model performance. We discuss the implications of both approaches in terms of credibility of simulated hydrological indicators for climate change adaptation. For that, we discuss and confirm the hypothesis that a good performance of hydrological models in the historical period increases confidence in projected impacts under climate change, and decreases uncertainty of projections related to hydrological models. Based on this, we find the second approach more trustworthy and recommend using it for impact assessment, especially if results are intended to support adaptation strategies. Guidelines for evaluation of global- and basin-scale models in the historical period, as well as criteria for model rejection from an ensemble as an outlier, are also suggested.